Tootfinder

Noticing a lot more coughing people on trains in #Japan recently too. Bet it's surging here too #Covid #covid19

From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
Miryeong Kwon, Donghyun Gouk, Junhyeok Jang, Jinwoo Baek, Hyunwoo You, Sangyoon Ji, Hongjoo Jung, Junseok Moon, Seungkwan Kang, Seungjun Lee, Myoungsoo Jung
https://arxiv.org/abs/2506.15613

From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
This paper explores how Compute Express Link (CXL) can transform PCIe-based block storage into a scalable, byte-addressable working memory. We address the challenges of adapting block storage to CXL's memory-centric model by emphasizing cacheability as a key enabler and advocating for Type 3 endpoint devices, referred to as CXL-SSDs. To validate our approach, we prototype a CXL-SSD on a custom FPGA platform and propose annotation mechanisms, Determinism and Bufferability, to enhance performance…

POCO: Scalable Neural Forecasting through Population Conditioning
Yu Duan, Hamza Tahir Chaudhry, Misha B. Ahrens, Christopher D Harvey, Matthew G Perich, Karl Deisseroth, Kanaka Rajan
https://arxiv.org/abs/2506.14957

POCO: Scalable Neural Forecasting through Population Conditioning
Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to closed-loop neurotechnology. While recent models of population activity emphasize interpretability and behavioral decoding, neural forecasting-particularly across multi-session, spontaneous recordings-remains underexplored. We introduce POCO, a unified forecasting model that combines a lightweight univariate forecaster with a population-level encoder to ca…

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang
https://arxiv.org/abs/2506.15223

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLI…

Hetero-Orbital Two-Component Fractional Quantum Hall States in Bilayer Graphene
Ke Huang, Ajit C. Balram, Hailong Fu, Chengqi Guo, Kenji Watanabe, Takashi Taniguchi, Jainendra K. Jain, Jun Zhu
https://arxiv.org/abs/2506.14188

Hetero-Orbital Two-Component Fractional Quantum Hall States in Bilayer Graphene
A two-dimensional electron system exposed to a strong magnetic field produces a plethora of strongly interacting fractional quantum Hall (FQH) states, the complex topological orders of which are revealed through exotic emergent particles, such as composite fermions, fractionally charged Abelian and non-Abelian anyons. Much insight has been gained by the study of multi-component FQH states, where spin and pseudospin indices of the electron contribute additional correlation. Traditional multi-com…

Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space
Sebastian Ehlert, Jan Hermann, Thijs Vogels, Victor Garcia Satorras, Stephanie Lanius, Marwin Segler, Derk P. Kooi, Kenji Takeda, Chin-Wei Huang, Giulia Luise, Rianne van den Berg, Paola Gori-Giorgi, Amir Karton
https://arxiv.org/abs/250…

Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space
Accurate thermochemical data with sub-chemical accuracy (i.e., within $\pm$1 kcal mol$^{-1}$ from sufficiently accurate experimental or theoretical reference data) is essential for the development and improvement of computational chemistry methods. Challenging thermochemical properties such as heats of formation and total atomization energies (TAEs) are of particular interest because they rigorously test the ability of computational chemistry methods to accurately describe complex chemical tran…

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu, Nicholas Konz, Yaqian Chen, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2506.12186

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representat…

Unveiling the galactic baryon cycle process by an empirical model
Yaoxin Chen, Xi Kang, Hong Guo, Hou-Zun Chen, Jiafeng Lu
https://arxiv.org/abs/2507.12209

Unveiling the galactic baryon cycle process by an empirical model
We propose an empirical model to describe and constrain the baryon cycle process during galaxy evolution. This model utilizes the evolution of star formation rate, derived from the stellar mass-halo mass relations (SHMRs) across different redshifts, and the cold gas content, derived from the NeutralUniverseMachine model, to constrain gas accretion and recycle of gas outflow in the model galaxy. Additionally, through detailed modeling of each cycle process, particularly the recycling process, an…

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2507.11569

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. However, their performance has mostly been tested in the context of rigid or less complex structures, such as the brain or abdominal organs, and it remains unclear whether these models can handle more challenging, deformable anatomy. Breast MRI registration is particularly difficult due to significant anatomical variation b…

Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Weichen Dai, Yuxuan Huang, Li Zhu, Dongjun Liu, Yu Zhang, Qibin Zhao, Andrzej Cichocki, Fabio Babiloni, Ke Li, Jianyu Qiu, Gangyong Jia, Wanzeng Kong, Qing Wu
https://arxiv.org/abs/2507.12417

Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Humans possess a remarkable capacity for spatial cognition, allowing for self-localization even in novel or unfamiliar environments. While hippocampal neurons encoding position and orientation are well documented, the large-scale neural dynamics supporting spatial representation, particularly during naturalistic, passive experience, remain poorly understood. Here, we demonstrate for the first time that non-invasive brain-computer interfaces (BCIs) based on electroencephalography (EEG) can decod…